Description

Tech

  • Regression modeling strategy
    • we use a property to characterise a response within groups
    • after specifying a regression structure, we optimize the model by some criteria
      • AIC, BIC … for time-consuming models + large-scaled data
      • AUROC, AUPRC … for prediction-focused + smaller-size data
    • if assumptions are satisfied with the optimized model, we are allowed to do inference on estimated responce and effects.
  • Longitudinal Data (serial data/repeated measures) Analysis
    • Model-based
      • General Least Squares (GLS)
      • Mixed Effect Model
      • Bayesian hierarchical models
    • Data-based
      • Summary Measures

Medical

  • 4 primary groups
    • non_pos: postive episodes from no-transplant patients
    • non_neg: test-negative and baseline time windows from ‘pure negative’ no-transplant patients
    • txp_pos: postive episodes from transplant patients
    • txp_neg: test-negative and baseline time windows from ‘pure negative’ transplant patients
  • An episode
    • from 24 hours before to 24 hours after a blood stream acquisition
  • Questions
    1. What’s the response trend of a feature during one ‘episode’ within groups?
    2. What’s the difference of response trend of a feature during one ‘episode’ across groups?

Report



Temp mean response

  • Check model assumptions
  • Partiel effect and contrast inference ***

Resp mean response

  • Check model assumptions
  • Partiel effect and contrast inference ***

Pulse mean response

  • Check model assumptions
  • Partiel effect and contrast inference ***

SBP mean response

  • Check model assumptions
  • Partiel effect and contrast inference ***

DBP mean response

  • Check model assumptions
  • Partiel effect and contrast inference ***

POTASSIUM mean response

  • Check model assumptions
  • Partiel effect and contrast inference ***

ALBUMIN mean response

  • Check model assumptions
  • Partiel effect and contrast inference ***

CALCIUM mean response

  • Check model assumptions
  • Partiel effect and contrast inference ***

SODIUM mean response

  • Check model assumptions
  • Partiel effect and contrast inference ***

WHITE_BLOOD_CELL_COUNT mean response

  • Check model assumptions
  • Partiel effect and contrast inference ***

PHOSPHORUS mean response

  • Check model assumptions
  • Partiel effect and contrast inference ***

PLATELET_COUNT mean response

  • Check model assumptions
  • Partiel effect and contrast inference ***

CO2 mean response

  • Check model assumptions
  • Partiel effect and contrast inference ***

PCO2 mean response

  • Check model assumptions
  • Partiel effect and contrast inference ***

CHLORIDE mean response

  • Check model assumptions
  • Partiel effect and contrast inference ***

BLOOD_UREA_NITROGEN mean response

  • Check model assumptions
  • Partiel effect and contrast inference ***

MAGNESIUM mean response

  • Check model assumptions
  • Partiel effect and contrast inference